Thinking Machines: A Survey of LLM based Reasoning Strategies

• URL: http://arxiv.org/abs/2503.10814v1
• Date: Thu, 13 Mar 2025 19:03:41 GMT
• Title: Thinking Machines: A Survey of LLM based Reasoning Strategies
• Authors: Dibyanayan Bandyopadhyay, Soham Bhattacharjee, Asif Ekbal,
• Abstract summary: Reasoning in Large Language Models (LLMs) aims to bridge this gap by enabling these models to think and re-evaluate their actions and responses.<n> Reasoning is an essential capability for complex problem-solving and a necessary step toward establishing trust in Artificial Intelligence (AI)<n>This will make AI suitable for deployment in sensitive domains, such as healthcare, banking, law, defense, security etc.
• Score: 33.08089616645845
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) are highly proficient in language-based tasks. Their language capabilities have positioned them at the forefront of the future AGI (Artificial General Intelligence) race. However, on closer inspection, Valmeekam et al. (2024); Zecevic et al. (2023); Wu et al. (2024) highlight a significant gap between their language proficiency and reasoning abilities. Reasoning in LLMs and Vision Language Models (VLMs) aims to bridge this gap by enabling these models to think and re-evaluate their actions and responses. Reasoning is an essential capability for complex problem-solving and a necessary step toward establishing trust in Artificial Intelligence (AI). This will make AI suitable for deployment in sensitive domains, such as healthcare, banking, law, defense, security etc. In recent times, with the advent of powerful reasoning models like OpenAI O1 and DeepSeek R1, reasoning endowment has become a critical research topic in LLMs. In this paper, we provide a detailed overview and comparison of existing reasoning techniques and present a systematic survey of reasoning-imbued language models. We also study current challenges and present our findings.

Related papers

• IOLBENCH: Benchmarking LLMs on Linguistic Reasoning [8.20398036986024]
  We introduce IOLBENCH, a novel benchmark derived from International Linguistics Olympiad (IOL) problems.<n>This dataset encompasses diverse problems testing syntax, morphology, phonology, and semantics.<n>We find that even the most advanced models struggle to handle the intricacies of linguistic complexity.
  arXiv  Detail & Related papers  (2025-01-08T03:15:10Z)
• Do great minds think alike? Investigating Human-AI Complementarity in Question Answering with CAIMIRA [43.116608441891096]
  Humans outperform AI systems in knowledge-grounded abductive and conceptual reasoning. State-of-the-art LLMs like GPT-4 and LLaMA show superior performance on targeted information retrieval.
  arXiv  Detail & Related papers  (2024-10-09T03:53:26Z)
• Proceedings of the First International Workshop on Next-Generation Language Models for Knowledge Representation and Reasoning (NeLaMKRR 2024) [16.282850445579857]
  Reasoning is an essential component of human intelligence as it plays a fundamental role in our ability to think critically. Recent leap forward in natural language processing, with the emergence of language models based on transformers, is hinting at the possibility that these models exhibit reasoning abilities. Despite ongoing discussions about what reasoning is in language models, it is still not easy to pin down to what extent these models are actually capable of reasoning.
  arXiv  Detail & Related papers  (2024-10-07T02:31:47Z)
• Should We Fear Large Language Models? A Structural Analysis of the Human Reasoning System for Elucidating LLM Capabilities and Risks Through the Lens of Heidegger's Philosophy [0.0]
  This study investigates the capabilities and risks of Large Language Models (LLMs) It uses the innovative parallels between the statistical patterns of word relationships within LLMs and Martin Heidegger's concepts of "ready-to-hand" and "present-at-hand" Our findings reveal that while LLMs possess the capability for Direct Explicative Reasoning and Pseudo Rational Reasoning, they fall short in authentic rational reasoning and have no creative reasoning capabilities.
  arXiv  Detail & Related papers  (2024-03-05T19:40:53Z)
• CLOMO: Counterfactual Logical Modification with Large Language Models [109.60793869938534]
  We introduce a novel task, Counterfactual Logical Modification (CLOMO), and a high-quality human-annotated benchmark. In this task, LLMs must adeptly alter a given argumentative text to uphold a predetermined logical relationship. We propose an innovative evaluation metric, the Self-Evaluation Score (SES), to directly evaluate the natural language output of LLMs.
  arXiv  Detail & Related papers  (2023-11-29T08:29:54Z)
• A Closer Look at the Self-Verification Abilities of Large Language Models in Logical Reasoning [73.77088902676306]
  We take a closer look at the self-verification abilities of large language models (LLMs) in the context of logical reasoning. Our main findings suggest that existing LLMs could struggle to identify fallacious reasoning steps accurately and may fall short of guaranteeing the validity of self-verification methods.
  arXiv  Detail & Related papers  (2023-11-14T07:13:10Z)
• Beyond Words: A Mathematical Framework for Interpreting Large Language Models [8.534513717370434]
  Large language models (LLMs) are powerful AI tools that can generate and comprehend natural language text and other complex information. We propose Hex a framework that clarifies key terms and concepts in LLM research, such as hallucinations, alignment, self-verification and chain-of-thought reasoning. We argue that our formal definitions and results are crucial for advancing the discussion on how to build generative AI systems.
  arXiv  Detail & Related papers  (2023-11-06T11:13:17Z)
• Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models [56.34029644009297]
  Large language models (LLMs) have demonstrated the ability to overcome various limitations of formal Knowledge Representation (KR) systems. LLMs excel most in abductive reasoning, followed by deductive reasoning, while they are least effective at inductive reasoning. We study single-task training, multi-task training, and "chain-of-thought" knowledge distillation fine-tuning technique to assess the performance of model.
  arXiv  Detail & Related papers  (2023-10-02T01:00:50Z)
• Re-Reading Improves Reasoning in Large Language Models [87.46256176508376]
  We introduce a simple, yet general and effective prompting method, Re2, to enhance the reasoning capabilities of off-the-shelf Large Language Models (LLMs) Unlike most thought-eliciting prompting methods, such as Chain-of-Thought (CoT), Re2 shifts the focus to the input by processing questions twice, thereby enhancing the understanding process. We evaluate Re2 on extensive reasoning benchmarks across 14 datasets, spanning 112 experiments, to validate its effectiveness and generality.
  arXiv  Detail & Related papers  (2023-09-12T14:36:23Z)
• Brain in a Vat: On Missing Pieces Towards Artificial General Intelligence in Large Language Models [83.63242931107638]
  We propose four characteristics of generally intelligent agents. We argue that active engagement with objects in the real world delivers more robust signals for forming conceptual representations. We conclude by outlining promising future research directions in the field of artificial general intelligence.
  arXiv  Detail & Related papers  (2023-07-07T13:58:16Z)
• A Survey of Large Language Models [81.06947636926638]
  Language modeling has been widely studied for language understanding and generation in the past two decades. Recently, pre-trained language models (PLMs) have been proposed by pre-training Transformer models over large-scale corpora. To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size.
  arXiv  Detail & Related papers  (2023-03-31T17:28:46Z)
• Shortcut Learning of Large Language Models in Natural Language Understanding [119.45683008451698]
  Large language models (LLMs) have achieved state-of-the-art performance on a series of natural language understanding tasks. They might rely on dataset bias and artifacts as shortcuts for prediction. This has significantly affected their generalizability and adversarial robustness.
  arXiv  Detail & Related papers  (2022-08-25T03:51:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.